Data Engineer II, AWS, Python, SQL

• Own and deliver data pipeline development and ongoing support for Property and GL pricing use cases
• Lead model implementation for Property and GL pricing models in partnership with Data Science and Actuarial teams
• Build and serve as the GL data subject matter expert to support benchmarks, modernization initiatives, and future enhancements
• Provide project leadership, planning, and execution across Property and GL data engineering initiatives
• Execute complex data preparation activities, including exploration, cleansing, and transformation, with awareness of enterprise architecture, platforms, and downstream consumption patterns
• Translate actuarial and data science requirements into scalable, production-ready data solutions
• Adopt and embed MLOps practices across the model development and implementation lifecycle
• Establish data quality controls, profiling, and monitoring for model implementation and ongoing performance
• Present analysis and technical recommendations to influence data architecture and implementation decisions
• Provide technical leadership through code reviews, design guidance, and mentoring within the team
• Apply agile methodologies to plan, prioritize, and deliver work across concurrent initiatives
• Perform other duties as assigned

• Bachelor’s Degree in STEM related field or equivalent.
• Eight years of related experience.
• Deep, hands-on experience with modern engineering tools and practices, including:
  Cloud platforms (preferably AWS) and model implementation in the cloud
• Programming in Python and SQL
• Working with data engineering concepts and building/maintaining data pipelines
• AI-native solutions and modern software engineering practices (e.g., APIs, microservices, test automation)
• Proven ability to deliver high-quality solutions at a steady, predictable pace:
  Breaks work into small, releasable increments Delivers complete, robust solutions while effectively managing tradeoffs
• Demonstrated domain expertise, including:
  Strong understanding of relevant technical concepts and industry trends
• In-depth knowledge of the systems you’ve worked on and familiarity with adjacent systems
• Strong problem-solving skills with a focus on building resilient, long-lived systems and finding innovative ways to resolve issues.
• Excellent written and verbal communication skills, with the ability to collaborate effectively with engineers, product partners, and business stakeholders.
• Proven experience leading or mentoring other engineers and helping to create a safe, inclusive environment where others can learn and grow.
• Self-motivated, with a track record of proactively identifying opportunities, driving improvements, and following through on team efforts.
• Preferred, Not
  
  Required:
  
  Experience in or exposure to the insurance industry, or a strong desire to learn the domain.
• Experience with Infrastructure as Code and Dev Ops tooling, such as Terraform and CI/CD pipelines.

Demonstrates expertise in data pipeline development and model implementation, with a strong focus on cloud platforms, particularly AWS. Proficient in Python and SQL, with a commitment to applying MLOps practices and agile methodologies to deliver high-quality, scalable data solutions.

• Data Pipeline Development
• Cloud Platforms (AWS)
• Python Programming
• SQL Programming
• MLOps Practices

• Data Engineering Concepts
• Model Implementation
• Data Preparation Activities
• AI-Native Solutions
• Test Automation
• Infrastructure as Code
• Dev Ops Tooling
• APIs
• Microservices
• Data Quality Controls

• Problem-Solving Skills
• Excellent Communication Skills
• Leadership
• Mentoring
• Self-Motivated

• Insurance Industry
• Actuarial
• Data Science
• Modern Software Engineering Practices
• Benchmarking

• Terraform
• CI/CD Pipelines
• Cloud Platforms
• Data Architecture
• Enterprise Architecture